2020 excess mortality & voting patterns in CH
Redistributed cantonal deaths
Data
Spatial
kt = read_rds("data/BfS/kt.Rds")
gg = read_rds("data/BfS/gg.Rds")
tg3o = read_rds("data/BfS/tg3o.Rds")
se_alt = read_rds("data/BfS/se_alt.Rds")Downscaled data
exp_deaths_2020_year_gem = read_rds("results/exp_deaths_2020_year_gem.Rds") %>%
select(-munici_excess_pop)Note the NA!
summary(exp_deaths_2020_year_gem$munici_excess_rat) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.3058 0.4167 0.4964 0.5714 4.0000 101
Ratio of excess deaths to expected deaths
Distribution
Maps
x <categorical>
# total N=2141 valid N=2040 mean=2.98 sd=1.43
Value | N | Raw % | Valid % | Cum. %
----------------------------------------------
[0.000,0.287) | 436 | 20.36 | 21.37 | 21.37
[0.287,0.365) | 389 | 18.17 | 19.07 | 40.44
[0.365,0.483) | 399 | 18.64 | 19.56 | 60.00
[0.483,0.625) | 409 | 19.10 | 20.05 | 80.05
[0.625,4.000] | 407 | 19.01 | 19.95 | 100.00
<NA> | 101 | 4.72 | <NA> | <NA>
Choropleth
Proportional symbols
Symbol size perceptually scaled to number of expected deaths.
EDA June vote
Map
Correlations
Unweighted
cor.test(exp_deaths_2020_year_gem$munici_excess_rat,
exp_deaths_2020_year_gem$vote_yes_jun_perc,
method = "pearson")
Pearson's product-moment correlation
data: exp_deaths_2020_year_gem$munici_excess_rat and exp_deaths_2020_year_gem$vote_yes_jun_perc
t = 5.0156, df = 2038, p-value = 0.0000005745
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.06734542 0.15308706
sample estimates:
cor
0.1104217
Weighted
wtd.cor(exp_deaths_2020_year_gem$munici_excess_rat,
exp_deaths_2020_year_gem$vote_yes_jun_perc,
weight = exp_deaths_2020_year_gem$munici_expected_med) correlation std.err t.value p.value
Y -0.02421592 0.02214474 -1.093529 0.2742907
Scatter
Unweighted
Weighted
Box
EDA Nov vote
Map
Correlations
Unweighted
cor.test(exp_deaths_2020_year_gem$munici_excess_rat,
exp_deaths_2020_year_gem$vote_yes_nov_perc,
method = "pearson")
Pearson's product-moment correlation
data: exp_deaths_2020_year_gem$munici_excess_rat and exp_deaths_2020_year_gem$vote_yes_nov_perc
t = 1.8844, df = 2038, p-value = 0.05965
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.001696124 0.084951179
sample estimates:
cor
0.04170594
Weighted
wtd.cor(exp_deaths_2020_year_gem$munici_excess_rat,
exp_deaths_2020_year_gem$vote_yes_nov_perc,
weight = exp_deaths_2020_year_gem$munici_expected_med) correlation std.err t.value p.value
Y -0.1597816 0.02186664 -7.307091 0.0000000000003899328